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Heuristics for the Variable Sized Bin Packing Problem Using a Hybrid P-System and CUDA Architecture

Qadha'a AlEnezi, Hosam AboElFotoh, Bader AlBdaiwi, Mohammad Ali AlMulla

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 560 M, P systems

March 3, 2016 by hgpu

Proteus: Exploiting Numerical Precision Variability in Deep Neural Networks

P. Judd, J. Albericio, N. Enright Jerger, A. Moshovos, T. Hetherington, T. Aamodt

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Tags: Computer science, Deep learning, GPGPU-sim, Neural networks

March 3, 2016 by hgpu

DeepSpark: Spark-Based Deep Learning Supporting Asynchronous Updates and Caffe Compatibility

Hanjoo Kim, Jaehong Park, Jaehee Jang, Sungroh Yoon

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Tags: Caffe, Computer science, CUDA, Deep learning, Distributed computing, Java, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 970, Package

March 1, 2016 by hgpu

Alpaka – An Abstraction Library for Parallel Kernel Acceleration

Erik Zenker, Benjamin Worpitz, Rene Widera, Axel Huebl, Guido Juckeland, Andreas Knupfer, Wolfgang E. Nagel, Michael Bussmann

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Tags: Computer science, CUDA, Mathematical Software, nVidia, OpenMP, Package, performance portability, Portability, Tesla K20, Tesla K80

March 1, 2016 by hgpu

Virtualizing Deep Neural Networks for Memory-Efficient Neural Network Design

Minsoo Rhu, Natalia Gimelshein, Jason Clemons, Arslan Zulfiqar, Stephen W. Keckler

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, Package, Tesla K40

March 1, 2016 by hgpu

DIANNE: Distributed Artificial Neural Networks for the Internet of Things

Elias De Coninck, Tim Verbelen, Bert Vankeirsbilck, Steven Bohez, Sam Leroux, Pieter Simoens

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Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, Java, Neural networks, nVidia, nVidia GeForce GTX 980, nVidia Jetson TK1, Package

February 25, 2016 by hgpu

GPU Robot Motion Planning using Semi-Infinite Nonlinear Programming

Benjamin Chretien, Adrien Escande, Abderrahmane Kheddar

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Tags: Computer science, CUDA, Nonlinear optimization, nVidia, nVidia GeForce GT 650 M, nVidia GeForce GTX Titan Black, Robotics, Tesla C2070

February 25, 2016 by hgpu

Auto-Vectorizing a Large-scale Production Unstructured-mesh CFD Application

G.R. Mudalige, I.Z. Reguly, M.B. Giles

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Tags: cfd, Code generation, Computer science, CUDA, Fluid dynamics, Fortran, nVidia, Programming techniques, Tesla K80

February 25, 2016 by hgpu

Parallel Approaches to Shortest-Path Problems for Multilevel Heterogeneous Computing

Hector Ortega Arranz

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 680, nVidia GeForce GTX Titan, Path problems, Thesis

February 25, 2016 by hgpu

Automatic Command Queue Scheduling for Task-Parallel Workloads in OpenCL

Ashwin M. Aji, Antonio J. Pena, Pavan Balaji, Wu-chun Feng

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Tags: Computer science, Heterogeneous systems, nVidia, OpenCL, Performance, Seismology, Task scheduling, Tesla C2050

February 23, 2016 by hgpu

VirtCL: a framework for OpenCL device abstraction and management

Yi-Ping You, Hen-Jung Wu, Yeh-Ning Tsai, Yen-Ting Chao

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Tags: Computer science, Heterogeneous systems, Memory model, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX 590, OpenCL, Performance

February 23, 2016 by hgpu

Deep Learning At Scale and At Ease

Wei Wang, Gang Chen, Haibo Chen, Tien Tuan Anh Dinh, Jinyang Gao, Beng Chin Ooi, Kian-Lee Tan, Sheng Wang

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Tags: Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce GTX 970, Package

February 23, 2016 by hgpu

CUDAnalyst (CUDA + Analyst)

Towards Feedback-to-Plan Decisions for Self-Evolving LLM Agents in CUDA Kernel Generation

CodegenBench

CodegenBench: Can LLMs Write Efficient Code Across Architectures?

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

Device Virtual Machine (DVM)

DVM: Real-Time Kernel Generation for Dynamic AI Models

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Agentic Code Optimization via Compiler-LLM Cooperation

See all packages

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high performance computing on graphics processing units: hgpu.org

Applications

Heuristics for the Variable Sized Bin Packing Problem Using a Hybrid P-System and CUDA Architecture

Proteus: Exploiting Numerical Precision Variability in Deep Neural Networks

DeepSpark: Spark-Based Deep Learning Supporting Asynchronous Updates and Caffe Compatibility

Alpaka – An Abstraction Library for Parallel Kernel Acceleration

Virtualizing Deep Neural Networks for Memory-Efficient Neural Network Design

DIANNE: Distributed Artificial Neural Networks for the Internet of Things

GPU Robot Motion Planning using Semi-Infinite Nonlinear Programming

Auto-Vectorizing a Large-scale Production Unstructured-mesh CFD Application

Parallel Approaches to Shortest-Path Problems for Multilevel Heterogeneous Computing

Automatic Command Queue Scheduling for Task-Parallel Workloads in OpenCL

VirtCL: a framework for OpenCL device abstraction and management

Deep Learning At Scale and At Ease

Recent source codes

CUDAnalyst (CUDA + Analyst)

CodegenBench

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CUDA Kernel Fusion Benchmarks

IntelliKit: Agent-first tooling for AMD hardware

DITRON: Distributed Compiler based on Triton for Parallel Systems

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

Device Virtual Machine (DVM)

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Agentic Code Optimization via Compiler-LLM Cooperation

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